Authors Santiago

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You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below πŸ‘‡

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through

You can get the answer using Bayes' theorem, but let's try to come up with it in a different β€”maybe more intuitiveβ€” way.


Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy


Let's now test both groups, starting with the 100 people sick:

▫️ 99 of them will be diagnosed (correctly) as sick (99%)

▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)


1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
Free machine learning education.

Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!

Here are 6 of these programs. Pick one and get started!


Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany

Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.

Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani

This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.

Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny

A theoretical course focusing on design principles and best practices to design deep neural networks.

Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov

A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.